Abstract: Real-time intrusion detection in virtual environments is crucial for maintaining the security and integrity of modern computing infrastructures. This paper proposes a nature-inspired mathematical model designed to detect both known and unknown attacks on virtual machines, focusing on enhancing detection accuracy and minimizing false alarm rates. The proposed model, named Developed Artificial Bee Colony Optimization Based on Cloud Model (DABCO_CM), is inspired by the foraging behavior of bee swarms and integrates principles from the Artificial Bee Colony algorithm and the cloud model rooted in fuzzy logic theory. The model was simulated using the UNSW_NB15 datasets in Google Colab and benchmarked against an existing model. It achieved a detection accuracy of 97.98%, compared to the existing model's 95.35%. Sensitivity results showed 99.92% for the proposed model, compared to 96.90% for the existing model, while specificity increased to 93.86%, in contrast to the existing model's 90.71%. These findings demonstrate a 3.02% increase in sensitivity, a 2.63% increase in accuracy, and a 3.15% increase in specificity, highlighting the model's superior capability in detecting attacks and its potential to learn from unlabeled data, addressing key challenges in virtual machine security.

Ayanseun S. Ayanboye, John E. Efiong, Temitope O. Ajayi, Rotimi A. Gbadebo, Bodunde O. Akinyemi, Emmanuel A. Olajubu and Ganiyu A. Aderounmu, “A Real-Time Nature-Inspired Intrusion Detection in Virtual Environments: An Artificial Bees Colony Approach Based on Cloud Model” International Journal of Advanced Computer Science and Applications(IJACSA), 15(12), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151266

@article{Ayanboye2024,
title = {A Real-Time Nature-Inspired Intrusion Detection in Virtual Environments: An Artificial Bees Colony Approach Based on Cloud Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151266},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151266},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {12},
author = {Ayanseun S. Ayanboye and John E. Efiong and Temitope O. Ajayi and Rotimi A. Gbadebo and Bodunde O. Akinyemi and Emmanuel A. Olajubu and Ganiyu A. Aderounmu}
}